@inproceedings{feng2025canvil,
author = {Feng, K. and Liao, Q. Vera and Xiao, Ziang and Vaughan, Jennifer Wortman and Zhang, Amy and McDonald, David W.},
title = {Canvil: Designerly Adaptation for LLM-Powered User Experiences},
booktitle = {CHI 2025},
year = {2025},
month = {April},
abstract = {Advancements in large language models (LLMs) are sparking a proliferation of LLM-powered user experiences (UX). In product teams, designers often craft UX to meet user needs, but it is unclear how they engage with LLMs as a novel design material. Through a formative study with 12 designers, we find that designers seek a translational process that enables design requirements to shape and be shaped by LLM behavior, motivating a need for designerly adaptation to facilitate this translation. We then built Canvil, a Figma widget that operationalizes designerly adaptation. We used Canvil as a probe to study designerly adaptation in a group-based design study (6 groups, N=17), finding that designers constructively iterated on both adaptation approaches and interface designs to enhance end-user interaction with LLMs. Furthermore, designers identified promising collaborative workflows for designerly adaptation. Our work opens new avenues for processes and tools that foreground designers' human-centered expertise when developing LLM-powered applications.},
url = {http://approjects.co.za/?big=en-us/research/publication/canvil-designerly-adaptation-for-llm-powered-user-experiences/},
}